OBJECTIVES: Burdens of mental disorders were 1.3 times larger in high-income countries (HICs) compared to the rest. This study aimed to identify social determinants associated with broad mental health outcomes in the Organizations for Economic Co-operation and Development (OECD) nations.

METHODS: An ecological study was conducted using the latest Institute for Health Metrics and Evaluation (IHME) Global Health Data Exchange (GHDx) and OECD data. The study size was 38 nations. Simple linear regression models were developed between DALYs caused by mental disorders and each social determinant (Gini coefficients, Social Institutions and Gender Index (SIGI), unemployment rates, hours needed to exit poverty, and Social Expenditure Database (SOCX)). The coefficients and Pearson’s correlations were calculated to examine the association. The explanatory power of the models was confirmed.

RESULTS: The regression models suggested that income Gini coefficients, gender disparities and hours needed to exit poverty were positively associated with the burden of mental disorders. SOCX was negatively associated with the burden of mental disorders. Hence greater social disparities in income or gender were associated with worsened mental health outcomes. Also, more difficulty exiting poverty might lead to deteriorating mental health outcomes. The nation's larger social spending was more likely to have improved mental health outcomes. Yet the findings suggested no association between the unemployment rates and mental health outcomes. Overall, the models' explanatory power was not strong in predicting mental health outcomes.

CONCLUSIONS: Despite a lack of explanatory power in the models, the study showed that macro-level social determinants might play a role in mental health outcomes. Further studies with robust evidence are required to establish the direction of effects and to investigate questions of causality.